Abstract

We introduce structured importance sampling, a new technique for efficient
ly rendering scenes illuminated by distant natural illumination given in an envi
ronment map. Our method handles occlusion, high-frequency lighting,
and it is significantly faster than alternative methods based on Monte Carlo sam
pling. We achieve this speedup as a result of several ideas.
First, we present a new metric for stratifying and sampling
an environment map taking into account both the illumination intensity
as well as the expected variance due to occlusion within the scene.
We then present a novel hierarchical stratification algorithm that uses our metr
ic to automatically stratify the environment map into regular strata. This appro
ach enables a number of rendering optimizations, such as pre-integrating the
illumination within each stratum to eliminate noise at the cost of adding
bias, and sorting the strata to reduce the number of sample rays. We have render
ed several scenes illuminated by natural lighting, and our results indicate that
structured importance sampling is better
than the best previous Monte Carlo techniques, requiring one to two
orders of magnitude fewer samples for the same image quality.